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   "source": [
    "# SageMaker PySpark Custom Estimator MNIST Example\n",
    "\n",
    "1. [Introduction](#Introduction)\n",
    "2. [Setup](#Setup)\n",
    "3. [Loading the Data](#Loading-the-Data)\n",
    "4. [Create a custom SageMakerEstimator](#Create-a-custom-SageMakerEstimator)\n",
    "5. [Inference](#Inference)\n",
    "6. [Clean-up](#Clean-up)\n",
    "7. [More on SageMaker Spark](#More-on-SageMaker-Spark)\n",
    "\n",
    "## Introduction\n",
    "This notebook will show how to cluster handwritten digits through the SageMaker PySpark library. \n",
    "\n",
    "We will manipulate data through Spark using a SparkSession, and then use the SageMaker Spark library to interact with SageMaker for training and inference. \n",
    "We will use a custom estimator to perform the classification task, and train and infer using that custom estimator.\n",
    "\n",
    "You can visit SageMaker Spark's GitHub repository at https://github.com/aws/sagemaker-spark to learn more about SageMaker Spark.\n",
    "\n",
    "This notebook was created and tested on an ml.m4.xlarge notebook instance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "First, we import the necessary modules and create the `SparkSession` with the SageMaker-Spark dependencies attached. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import boto3\n",
    "\n",
    "from pyspark import SparkContext, SparkConf\n",
    "from pyspark.sql import SparkSession\n",
    "\n",
    "import sagemaker\n",
    "from sagemaker import get_execution_role\n",
    "import sagemaker_pyspark\n",
    "\n",
    "role = get_execution_role()\n",
    "\n",
    "# Configure Spark to use the SageMaker Spark dependency jars\n",
    "jars = sagemaker_pyspark.classpath_jars()\n",
    "\n",
    "classpath = \":\".join(sagemaker_pyspark.classpath_jars())\n",
    "\n",
    "# See the SageMaker Spark Github to learn how to connect to EMR from a notebook instance\n",
    "spark = SparkSession.builder.config(\"spark.driver.extraClassPath\", classpath)\\\n",
    "    .master(\"local[*]\").getOrCreate()\n",
    "    \n",
    "spark"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Data\n",
    "\n",
    "Now, we load the MNIST dataset into a Spark Dataframe, which dataset is available in LibSVM format at\n",
    "\n",
    "`s3://sagemaker-sample-data-[region]/spark/mnist/`\n",
    "\n",
    "where `[region]` is replaced with a supported AWS region, such as us-east-1.\n",
    "\n",
    "In order to train and make inferences our input DataFrame must have a column of Doubles (named \"label\" by default) and a column of Vectors of Doubles (named \"features\" by default).\n",
    "\n",
    "Spark's LibSVM DataFrameReader loads a DataFrame already suitable for training and inference.\n",
    "\n",
    "Here, we load into a DataFrame in the SparkSession running on the local Notebook Instance, but you can connect your Notebook Instance to a remote Spark cluster for heavier workloads. Starting from EMR 5.11.0, SageMaker Spark is pre-installed on EMR Spark clusters. For more on connecting your SageMaker Notebook Instance to a remote EMR cluster, please see [this blog post](https://aws.amazon.com/blogs/machine-learning/build-amazon-sagemaker-notebooks-backed-by-spark-in-amazon-emr/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "\n",
    "region = boto3.Session().region_name\n",
    "spark._jsc.hadoopConfiguration().set('fs.s3a.endpoint', 's3.{}.amazonaws.com'.format(region))\n",
    "\n",
    "trainingData = spark.read.format('libsvm')\\\n",
    "    .option('numFeatures', '784')\\\n",
    "    .load('s3a://sagemaker-sample-data-{}/spark/mnist/train/'.format(region))\n",
    "\n",
    "testData = spark.read.format('libsvm')\\\n",
    "    .option('numFeatures', '784')\\\n",
    "    .load('s3a://sagemaker-sample-data-{}/spark/mnist/test/'.format(region))\n",
    "\n",
    "trainingData.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MNIST images are 28x28, resulting in 784 pixels. The dataset consists of images of digits going from 0 to 9, representing 10 classes. \n",
    "\n",
    "In each row:\n",
    "* The `label` column identifies the image's label. For example, if the image of the handwritten number is the digit 5, the label value is 5.\n",
    "* The `features` column stores a vector (`org.apache.spark.ml.linalg.Vector`) of `Double` values. The length of the vector is 784, as each image consists of 784 pixels. Those pixels are the features we will use. \n",
    "\n",
    "\n",
    "\n",
    "As we are interested in clustering the images of digits, the number of pixels represents the feature vector, while the number of classes represents the number of clusters we want to find. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a custom SageMakerEstimator\n",
    "\n",
    "SageMaker-Spark provides several classes that extend SageMakerEstimator to use SageMaker-provided algorithms, like `KMeansSageMakerEstimator` to run the SageMaker-provided K-Means algorithm. These classes are `SageMakerEstimator` with certain default values passed in. You can use SageMaker-Spark with any algorithm (provided by Amazon or your own model) that runs on Amazon SageMaker by creating a `SageMakerEstimator`.\n",
    "\n",
    "In this example, we'll re-create the `KMeansSageMakerEstimator` into an equivalent SageMakerEstimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
    "from sagemaker_pyspark import SageMakerEstimator\n",
    "from sagemaker_pyspark.transformation.deserializers import KMeansProtobufResponseRowDeserializer\n",
    "from sagemaker_pyspark.transformation.serializers import ProtobufRequestRowSerializer\n",
    "from sagemaker_pyspark import IAMRole\n",
    "from sagemaker_pyspark import RandomNamePolicyFactory\n",
    "from sagemaker_pyspark import EndpointCreationPolicy\n",
    "\n",
    "# Create an Estimator from scratch\n",
    "estimator = SageMakerEstimator(\n",
    "    trainingImage = get_image_uri(region, 'kmeans'), # Training image \n",
    "    modelImage = get_image_uri(region, 'kmeans'), # Model image\n",
    "    requestRowSerializer = ProtobufRequestRowSerializer(),\n",
    "    responseRowDeserializer = KMeansProtobufResponseRowDeserializer(),\n",
    "    hyperParameters = {\"k\": \"10\", \"feature_dim\": \"784\"}, # Set parameters for K-Means\n",
    "    sagemakerRole = IAMRole(role),\n",
    "    trainingInstanceType = \"ml.m4.xlarge\",\n",
    "    trainingInstanceCount = 1,\n",
    "    endpointInstanceType = \"ml.t2.medium\",\n",
    "    endpointInitialInstanceCount = 1,\n",
    "    trainingSparkDataFormat = \"sagemaker\",\n",
    "    namePolicyFactory = RandomNamePolicyFactory(\"sparksm-4-\"),\n",
    "    endpointCreationPolicy = EndpointCreationPolicy.CREATE_ON_TRANSFORM\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main parts of a `SageMakerEstimator` are:\n",
    "* `trainingImage`: the Docker Registry path where the training image is hosted - can be a custom Docker image hosting your own model, or one of the [Amazon provided images](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html)\n",
    "* `modelImage`: the Docker Registry path where the inference image is used - can be a custom Docker image hosting your own model, or one of the [Amazon provided images](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html)\n",
    "* `hyperparameters`: the hyper-parameters of the algorithm being created - the values passed in need to be of type string\n",
    "\n",
    "To put this `SageMakerEstimator` back into context, let's look at the below architecture that shows what actually runs on the notebook instance and on SageMaker."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Custom estimator on SageMaker](img/sagemaker-spark-custom-architecture.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train this estimator by calling fit on it with the training data. Please note the below code will take several minutes to run and create all the resources needed for this model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "customModel = estimator.fit(trainingData)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference\n",
    "\n",
    "Now we transform our DataFrame.\n",
    "To do this, we serialize each row's \"features\" Vector of Doubles into a Protobuf format for inference against the Amazon SageMaker Endpoint. We deserialize the Protobuf responses back into our DataFrame. This serialization and deserialization is handled automatically by the `transform()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformedData = customModel.transform(testData)\n",
    "transformedData.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How well did the custom algorithm perform? Let us display the digits from each of the clusters and manually inspect the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.types import DoubleType\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import string\n",
    "\n",
    "# Helper function to display a digit\n",
    "def showDigit(img, caption='', xlabel='', subplot=None):\n",
    "    if subplot==None:\n",
    "        _,(subplot)=plt.subplots(1,1)\n",
    "    imgr=img.reshape((28,28))\n",
    "    subplot.axes.get_xaxis().set_ticks([])\n",
    "    subplot.axes.get_yaxis().set_ticks([])\n",
    "    plt.title(caption)\n",
    "    plt.xlabel(xlabel)\n",
    "    subplot.imshow(imgr, cmap='gray')\n",
    "    \n",
    "def displayClusters(data):\n",
    "    images = np.array(data.select(\"features\").cache().take(250))\n",
    "    clusters = data.select(\"closest_cluster\").cache().take(250)\n",
    "\n",
    "    for cluster in range(10):\n",
    "        print('\\n\\n\\nCluster {}:'.format(string.ascii_uppercase[cluster]))\n",
    "        digits = [ img for l, img in zip(clusters, images) if int(l.closest_cluster) == cluster ]\n",
    "        height=((len(digits)-1)//5)+1\n",
    "        width=5\n",
    "        plt.rcParams[\"figure.figsize\"] = (width,height)\n",
    "        _, subplots = plt.subplots(height, width)\n",
    "        subplots=np.ndarray.flatten(subplots)\n",
    "        for subplot, image in zip(subplots, digits):\n",
    "            showDigit(image, subplot=subplot)\n",
    "        for subplot in subplots[len(digits):]:\n",
    "            subplot.axis('off')\n",
    "\n",
    "        plt.show()\n",
    "        \n",
    "displayClusters(transformedData)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean-up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we don't need to make any more inferences, now we delete the resources (endpoints, models, configurations, etc):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete the resources\n",
    "from sagemaker_pyspark import SageMakerResourceCleanup\n",
    "\n",
    "def cleanUp(model):\n",
    "    resource_cleanup = SageMakerResourceCleanup(model.sagemakerClient)\n",
    "    resource_cleanup.deleteResources(model.getCreatedResources())\n",
    "\n",
    "cleanUp(customModel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More on SageMaker Spark\n",
    "\n",
    "The SageMaker Spark Github repository has more about SageMaker Spark, including how to use SageMaker Spark using the Scala SDK: https://github.com/aws/sagemaker-spark\n"
   ]
  }
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  "notice": "Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.  Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
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